由于气候变化以及人类活动的影响,鄱阳湖水文情势发生了一定程度的变化,需定量评估不同驱动因子对鄱阳湖水情演变的影响。采用一系列水文指标表征鄱阳湖水文情势,构建BP神经网络模型模拟鄱阳湖水位,通过情境对比分析长江干流流量、鄱阳湖子流域入湖流量以及地形变化对各水文指标变化的贡献率。结果表明:长江干流流量是鄱阳湖7—10月份月平均水位降低的主要驱动因子,贡献率为52%～67%,对年最高极值水位的拉低效应明显,对年最低极值水位起到一定的抬高作用;地形是鄱阳湖12月—翌年3月月平均水位下降的主要驱动因子,贡献率为92%～185%,对年最低极值水位的拉低效应明显。最后对鄱阳湖水资源管理和调控提出一些建议:优化三峡水库运行调度,根据实际情况将三峡水库汛末蓄水时间适当提前;加强鄱阳湖子流域水利工程建设,在三峡蓄水期间增加五河泄流;规范采砂。
Abstract
The hydrologic regime of the Poyang Lake has changed due to the impact of climate change and human activities. The influence of different driving factors on the hydrologic regime evolution of the Poyang Lake needs to be quantitatively evaluated. With a series of hydrological indicators characterizing the hydrological situation of Poyang Lake, a BP neural network model was constructed to simulate the water level of the Poyang Lake. The rates of contribution of flow rate of mainstream Yangtze River, inflow from sub-basins to the Poyang Lake, and topographic changes to the variation of hydrological indicators are studied through scenario analysis. Results manifest that the flow rate of mainstream Yangtze River is the major driving factor for the decline of the average water level of Poyang Lake from July to October, with a contribution rate of 52%-67%; the flow rate of mainstream Yangtze River cut obviously the annual maximum extreme water level while boosted the annual minimum extreme water level. Topography is the main driving factor for the decrease of average water level of the Poyang Lake from December to next March, with a contribution rate of 92% to 185%. Topography also brought down the minimum extreme water level apparently. In addition, some suggestions are put forward for the management and regulation of water resources in the Poyang Lake. The operation of the Three Gorges Reservoir should be optimized, and the water storage of the Three Gorges Reservoir should be appropriately advanced in time according to actual situation. The construction of water conservancy projects in the Poyang Lake sub-basins should be strengthened, and the discharge from five rivers during the impoundment of the Three Gorges Reservoir can be enhanced. Sand mining must also be regulated.
关键词
水情演变 /
BP神经网络模型 /
水文因子 /
地形因子 /
贡献率 /
鄱阳湖
Key words
hydrologic regime evolution /
BP neural network model /
hydrologic factor /
topographic factor /
contribution rate /
Poyang Lake
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基金
江西省水利厅科技项目(KT201701,KT201538)
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